Competitive Algorithms for Online Budget-Constrained Continuous DR-Submodular Problems

Sadeghi, Omid, Eghbali, Reza, Fazel, Maryam

arXiv.org Machine Learning 

In this paper, we study a certain class of online optimization problems, where the goal is to maximize a function that is not necessarily concave and satisfies the Diminishing Returns (DR) property under budget constraints. We analyze a primal-dual algorithm, called the Generalized Sequential algorithm, and we obtain the first bound on the competitive ratio of online monotone DR-submodular function maximization subject to linear packing constraints which matches the known tight bound in the special case of linear objective function.

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